Fast planning through planning graph analysis
Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Functional strips: a more flexible language for planning and problem solving
Logic-based artificial intelligence
STeLLa: An Optimal Sequential and Parallel Planner
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
A Heuristic for Planning Based on Action Evaluation
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
A Heuristic for Domain Independent Planning and Its Use in an Enforced Hill-Climbing Algorithm
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Multiobjective heuristic state-space planning
Artificial Intelligence
SAT-based planning in complex domains: concurrency, constraints and nondeterminism
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Propositional planning in BDI agents
Proceedings of the 2004 ACM symposium on Applied computing
Coordinating Self-interested Planning Agents
Autonomous Agents and Multi-Agent Systems
Constraint partitioning in penalty formulations for solving temporal planning problems
Artificial Intelligence
Decomposition of planning problems
AI Communications
Modelling and solving English Peg Solitaire
Computers and Operations Research
Plan-Coordination Mechanisms and the Price of Autonomy
Computational Logic in Multi-Agent Systems
A Global Filtration for Satisfying Goals in Mutual Exclusion Networks
Recent Advances in Constraints
Learning from planner performance
Artificial Intelligence
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
State agnostic planning graphs and the application to belief-space planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Using memory to transform search on the planning graph
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
The GRT planning system: backward heuristic construction in forward state-space planning
Journal of Artificial Intelligence Research
Temporal planning with mutual exclusion reasoning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
The detection and exploitation of symmetry in planning problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Constraint partitioning in penalty formulations for solving temporal planning problems
Artificial Intelligence
Planned and traversable play-out: a flexible method for executing scenario-based programs
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
SPIN'03 Proceedings of the 10th international conference on Model checking software
A meta-CSP model for optimal planning
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
A hybrid deliberative layer for robotic agents: fusing DL reasoning with HTN planning in autonomous robots
Planning in domains with derived predicates through rule-action graphs and local search
Annals of Mathematics and Artificial Intelligence
A conformant planner based on approximation: CpA(H)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Hi-index | 0.00 |
STAN is a Graphplan-based planner, so-called because it uses a variety of STate ANalysis techniques to enhance its performance. STAN competed in the AIPS-98 planning competition where it compared well with the other competitors in terms of speed, finding solutions fastest to many of the problems posed. Although the domain analysis techniques STAN exploits are an important factor in its overall performance, we believe that the speed at which STAN solved the competition problems is largely due to the implementation of its plan graph. The implementation is based on two insights: that many of the graph construction operations can be implemented as bit-level logical operations on bit vectors, and that the graph should not be explicitly constructed beyond the fix point. This paper describes the implementation of STAN's plan graph and provides experimental results which demonstrate the circumstances under which advantages can be obtained from using this implementation.